In recent years, display apparatus, such as televisions (TVs) are increasingly using set-top boxes rather than directly receiving broadcast signals. In this case, a display apparatus cannot know what content is currently viewed by a user.
If the display apparatus knows what content is currently viewed by the user, smart services, such as targeting advertisements, content recommendation, related-information services can be provided. To achieve this, automatic content recognition (ACR) which is technology for recognizing a currently displayed content at a display apparatus is developed.
In a related-art method, a display apparatus periodically captures a screen which is being currently viewed, extracts a characteristic for recognizing the screen, and periodically requests a server to recognize the current screen through a query.
However, the display apparatus has no choice but to frequently send a query to the server in order to rapidly detect a change in the content which is being viewed, and accordingly, ACR requires many resources and much cost.
With the development of computer technology, data traffic increases in the form of an exponential function, and artificial intelligence (AI) becomes an important trend that leads future innovation. Since AI simulates the way the human thinks, it can be applied to all industries infinitely.
The AI system refers to a computer system that implements high intelligence as human intelligence, and is a system that makes a machine learn and determine by itself and become smarter unlike an existing rule-based smart system. The AI system can enhance a recognition rate as it is used and can exactly understand user's taste, and thus the existing rule-based smart system is increasingly being replaced with a deep-learning based-AI system.
The AI technology includes machine learning (for example, deep learning) and element technology using machine learning.
Machine learning is algorithm technology for classifying/learning characteristics of input data by itself, and element technology is technology for simulating functions of the human brain, such as recognizing, determining, or the like by utilizing a machine learning algorithm, such as deep learning, and may include technical fields, such as linguistic understanding, visual understanding, inference/prediction, knowledge representation, operation control, or the like.
Various fields to which the AI technology is applied are as follows. The linguistic understanding is technology for recognizing human languages/characters and applying/processing the same, and may include natural language processing, machine translation, a dialog system, question and answer, voice recognition/synthesis. The visual understanding is technology for recognizing things in the same way as humans do with eyes, and may include object recognition, object tracking, image search, people recognition, scene understanding, space understanding, and image enhancement. The inference/prediction is technology for inferring and predicting logically by determining information, and may include knowledge/probability-based inference, optimization prediction, preference-based planning, recommendation, or the like. The knowledge representation is technology for automating human experience information into knowledge data, and may include knowledge establishment (data generation/classification), knowledge management (data utilization), or the like. The operation control is technology for controlling autonomous driving of vehicles and a motion of a robot, and may include motion control (navigation, collision, driving), manipulation control (behavior control), or the like.
The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the present disclosure.